PPT - Mining of Massive Datasets
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Mining of Massive Datasets
Jure Leskovec, Anand Rajaraman, Jeff Ullman
Stanford University
http://www.mmds.org
High dim.
data
Graph
data
Infinite
data
Machine
learning
Apps
Locality
sensitive
hashing
PageRank,
SimRank
Filtering
data
streams
SVM
Recommen
der systems
Clustering
Community
Detection
Queries on
streams
Decision
Trees
Association
Rules
Dimensional
ity
reduction
Spam
Detection
Web
advertising
Perceptron,
kNN
Duplicate
document
detection
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
2
In many data mining situations, we do not
know the entire data set in advance
Stream Management is important when the
input rate is controlled externally:
Google queries
Twitter or Facebook status updates
We can think of the data as infinite and
non-stationary (the distribution changes
over time)
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
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Input elements enter at a rapid rate,
at one or more input ports (i.e., streams)
We call elements of the stream tuples
The system cannot store the entire stream
accessibly
Q: How do you make critical calculations
about the stream using a limited amount of
(secondary) memory?
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
4
Stochastic Gradient Descent (SGD) is an
example of a stream algorithm
In Machine Learning we call this: Online Learning
Allows for modeling problems where we have
a continuous stream of data
We want an algorithm to learn from it and
slowly adapt to the changes in data
Idea: Do slow updates to the model
SGD (SVM, Perceptron) makes small updates
So: First train the classifier on training data.
Then: For every example from the stream, we slightly
update the model (using small learning rate)
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
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Ad-Hoc
Queries
Standing
Queries
. . . 1, 5, 2, 7, 0, 9, 3
Output
. . . a, r, v, t, y, h, b
. . . 0, 0, 1, 0, 1, 1, 0
time
Streams Entering.
Each is stream is
composed of
elements/tuples
Processor
Limited
Working
Storage
Archival
Storage
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
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Types of queries one wants on answer on
a data stream: (we’ll do these today)
Sampling data from a stream
Construct a random sample
Queries over sliding windows
Number of items of type x in the last k elements
of the stream
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
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Types of queries one wants on answer on
a data stream: (we’ll do these next time)
Filtering a data stream
Select elements with property x from the stream
Counting distinct elements
Number of distinct elements in the last k elements
of the stream
Estimating moments
Estimate avg./std. dev. of last k elements
Finding frequent elements
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
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Mining query streams
Google wants to know what queries are
more frequent today than yesterday
Mining click streams
Yahoo wants to know which of its pages are
getting an unusual number of hits in the past hour
Mining social network news feeds
E.g., look for trending topics on Twitter, Facebook
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
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Sensor Networks
Many sensors feeding into a central controller
Telephone call records
Data feeds into customer bills as well as
settlements between telephone companies
IP packets monitored at a switch
Gather information for optimal routing
Detect denial-of-service attacks
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
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As the stream grows the sample
also gets bigger
Since we can not store the entire stream,
one obvious approach is to store a sample
Two different problems:
(1) Sample a fixed proportion of elements
in the stream (say 1 in 10)
(2) Maintain a random sample of fixed size
over a potentially infinite stream
At any “time” k we would like a random sample
of s elements
What is the property of the sample we want to maintain?
For all time steps k, each of k elements seen so far has
equal prob. of being sampled
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
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Problem 1: Sampling fixed proportion
Scenario: Search engine query stream
Stream of tuples: (user, query, time)
Answer questions such as: How often did a user
run the same query in a single days
Have space to store 1/10th of query stream
Naïve solution:
Generate a random integer in [0..9] for each query
Store the query if the integer is 0, otherwise
discard
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
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Simple question: What fraction of queries by an
average search engine user are duplicates?
Suppose each user issues x queries once and d queries
twice (total of x+2d queries)
Correct answer: d/(x+d)
Proposed solution: We keep 10% of the queries
Sample will contain x/10 of the singleton queries and
2d/10 of the duplicate queries at least once
But only d/100 pairs of duplicates
d/100 = 1/10 ∙ 1/10 ∙ d
Of d “duplicates” 18d/100 appear exactly once
18d/100 = ((1/10 ∙ 9/10)+(9/10 ∙ 1/10)) ∙ d
So the sample-based answer is
𝑑
100
𝑥
𝑑 18𝑑
+
+
10 100 100
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
=
𝒅
𝟏𝟎𝒙+𝟏𝟗𝒅
14
Solution:
Pick 1/10th of users and take all their
searches in the sample
Use a hash function that hashes the
user name or user id uniformly into 10
buckets
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
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Stream of tuples with keys:
Key is some subset of each tuple’s components
e.g., tuple is (user, search, time); key is user
Choice of key depends on application
To get a sample of a/b fraction of the stream:
Hash each tuple’s key uniformly into b buckets
Pick the tuple if its hash value is at most a
Hash table with b buckets, pick the tuple if its hash value is at most a.
How to generate a 30% sample?
Hash into b=10 buckets, take the tuple if it hashes to one of the first 3 buckets
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
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As the stream grows, the sample is of
fixed size
Problem 2: Fixed-size sample
Suppose we need to maintain a random
sample S of size exactly s tuples
E.g., main memory size constraint
Why? Don’t know length of stream in advance
Suppose at time n we have seen n items
Each item is in the sample S with equal prob. s/n
How to think about the problem: say s = 2
Stream: a x c y z k c d e g…
At n= 5, each of the first 5 tuples is included in the sample S with equal prob.
At n= 7, each of the first 7 tuples is included in the sample S with equal prob.
Impractical solution would be to store all the n tuples seen
so far and out of them pick s at random
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
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Algorithm (a.k.a. Reservoir Sampling)
Store all the first s elements of the stream to S
Suppose we have seen n-1 elements, and now
the nth element arrives (n > s)
With probability s/n, keep the nth element, else discard it
If we picked the nth element, then it replaces one of the
s elements in the sample S, picked uniformly at random
Claim: This algorithm maintains a sample S
with the desired property:
After n elements, the sample contains each
element seen so far with probability s/n
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
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We prove this by induction:
Assume that after n elements, the sample contains
each element seen so far with probability s/n
We need to show that after seeing element n+1
the sample maintains the property
Sample contains each element seen so far with
probability s/(n+1)
Base case:
After we see n=s elements the sample S has the
desired property
Each out of n=s elements is in the sample with
probability s/s = 1
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
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Inductive hypothesis: After n elements, the sample
S contains each element seen so far with prob. s/n
Now element n+1 arrives
Inductive step: For elements already in S,
probability that the algorithm keeps it in S is:
s s s 1
n
1
s n 1
n 1 Element
n n+11 Element
in the
Element n+1 discarded
not discarded
sample not picked
So, at time n, tuples in S were there with prob. s/n
Time nn+1, tuple stayed in S with prob. n/(n+1)
𝒔
𝒏
𝒔
So prob. tuple is in S at time n+1 = ⋅
=
𝒏 𝒏+𝟏
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
𝒏+𝟏
21
A useful model of stream processing is that
queries are about a window of length N –
the N most recent elements received
Interesting case: N is so large that the data
cannot be stored in memory, or even on disk
Or, there are so many streams that windows
for all cannot be stored
Amazon example:
For every product X we keep 0/1 stream of whether
that product was sold in the n-th transaction
We want answer queries, how many times have we
sold X in the last k sales
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
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Sliding window on a single stream:
N=6
qwertyuiopasdfghjklzxcvbnm
qwertyuiopasdfghjklzxcvbnm
qwertyuiopasdfghjklzxcvbnm
qwertyuiopasdfghjklzxcvbnm
Past
Future
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
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Problem:
Given a stream of 0s and 1s
Be prepared to answer queries of the form
How many 1s are in the last k bits? where k ≤ N
Obvious solution:
Store the most recent N bits
When new bit comes in, discard the N+1st bit
010011011101010110110110
Past
Suppose N=6
Future
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
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You can not get an exact answer without
storing the entire window
Real Problem:
What if we cannot afford to store N bits?
E.g., we’re processing 1 billion streams and
N = 1 billion 0 1 0 0 1 1 0 1 1 1 0 1 0 1 0 1 1 0 1 1 0 1 1 0
Past
Future
But we are happy with an approximate
answer
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
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Q: How many 1s are in the last N bits?
A simple solution that does not really solve
our problem: Uniformity assumption
N
010011100010100100010110110111001010110011010
Past
Future
Maintain 2 counters:
S: number of 1s from the beginning of the stream
Z: number of 0s from the beginning of the stream
How many 1s are in the last N bits? 𝑵 ∙
But, what if stream is non-uniform?
𝑺
𝑺+𝒁
What if distribution changes over time?
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
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[Datar, Gionis, Indyk, Motwani]
DGIM solution that does not assume
uniformity
We store 𝑶(log𝟐𝑵) bits per stream
Solution gives approximate answer,
never off by more than 50%
Error factor can be reduced to any fraction > 0,
with more complicated algorithm and
proportionally more stored bits
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
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Solution that doesn’t (quite) work:
Summarize exponentially increasing regions
of the stream, looking backward
Drop small regions if they begin at the same point
Window of as a larger region
width 16
has 6 1s
6
?
10
4
3
2
2
1
1 0
010011100010100100010110110111001010110011010
N
We can reconstruct the count of the last N bits, except we
are not sure how many of the last 6 1s are included in the N
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
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Stores only O(log2N ) bits
𝑶(log 𝑵) counts of log 𝟐 𝑵 bits each
Easy update as more bits enter
Error in count no greater than the number
of 1s in the “unknown” area
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
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As long as the 1s are fairly evenly distributed,
the error due to the unknown region is small
– no more than 50%
But it could be that all the 1s are in the
unknown area at the end
In that case, the error is unbounded!
6
?
10
4
3
2
2
1
1 0
010011100010100100010110110111001010110011010
N
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
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[Datar, Gionis, Indyk, Motwani]
Idea: Instead of summarizing fixed-length
blocks, summarize blocks with specific
number of 1s:
Let the block sizes (number of 1s) increase
exponentially
When there are few 1s in the window, block
sizes stay small, so errors are small
1001010110001011010101010101011010101010101110101010111010100010110010
N
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
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Each bit in the stream has a timestamp,
starting 1, 2, …
Record timestamps modulo N (the window
size), so we can represent any relevant
timestamp in 𝑶(𝒍𝒐𝒈𝟐 𝑵) bits
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
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A bucket in the DGIM method is a record
consisting of:
(A) The timestamp of its end [O(log N) bits]
(B) The number of 1s between its beginning and
end [O(log log N) bits]
Constraint on buckets:
Number of 1s must be a power of 2
That explains the O(log log N) in (B) above
1001010110001011010101010101011010101010101110101010111010100010110010
N
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
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Either one or two buckets with the same
power-of-2 number of 1s
Buckets do not overlap in timestamps
Buckets are sorted by size
Earlier buckets are not smaller than later buckets
Buckets disappear when their
end-time is > N time units in the past
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
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At least 1 of
size 16. Partially
beyond window.
2 of
size 8
2 of
size 4
1 of
size 2
2 of
size 1
1001010110001011010101010101011010101010101110101010111010100010110010
N
Three properties of buckets that are maintained:
- Either one or two buckets with the same power-of-2 number of 1s
- Buckets do not overlap in timestamps
- Buckets are sorted by size
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
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When a new bit comes in, drop the last
(oldest) bucket if its end-time is prior to N
time units before the current time
2 cases: Current bit is 0 or 1
If the current bit is 0:
no other changes are needed
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
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If the current bit is 1:
(1) Create a new bucket of size 1, for just this bit
End timestamp = current time
(2) If there are now three buckets of size 1,
combine the oldest two into a bucket of size 2
(3) If there are now three buckets of size 2,
combine the oldest two into a bucket of size 4
(4) And so on …
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
38
Current state of the stream:
1001010110001011010101010101011010101010101110101010111010100010110010
Bit of value 1 arrives
0010101100010110101010101010110101010101011101010101110101000101100101
Two orange buckets get merged into a yellow bucket
0010101100010110101010101010110101010101011101010101110101000101100101
Next bit 1 arrives, new orange bucket is created, then 0 comes, then 1:
0101100010110101010101010110101010101011101010101110101000101100101101
Buckets get merged…
0101100010110101010101010110101010101011101010101110101000101100101101
State of the buckets after merging
0101100010110101010101010110101010101011101010101110101000101100101101
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
39
To estimate the number of 1s in the most
recent N bits:
1. Sum the sizes of all buckets but the last
(note “size” means the number of 1s in the bucket)
2. Add half the size of the last bucket
Remember: We do not know how many 1s
of the last bucket are still within the wanted
window
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
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At least 1 of
size 16. Partially
beyond window.
2 of
size 8
2 of
size 4
1 of
size 2
2 of
size 1
1001010110001011010101010101011010101010101110101010111010100010110010
N
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
41
Why is error 50%? Let’s prove it!
Suppose the last bucket has size 2r
Then by assuming 2r-1 (i.e., half) of its 1s are
still within the window, we make an error of
at most 2r-1
Since there is at least one bucket of each of
the sizes less than 2r, the true sum is at least
1 + 2 + 4 + .. + 2r-1 = 2r -1
At least 16 1s
Thus, error at most 50%
111111110000000011101010101011010101010101110101010111010100010110010
N
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
42
Instead of maintaining 1 or 2 of each size
bucket, we allow either r-1 or r buckets (r > 2)
Except for the largest size buckets; we can have
any number between 1 and r of those
Error is at most O(1/r)
By picking r appropriately, we can tradeoff
between number of bits we store and the
error
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
43
Can we use the same trick to answer queries
How many 1’s in the last k? where k < N?
A: Find earliest bucket B that at overlaps with k.
Number of 1s is the sum of sizes of more recent
buckets + ½ size of B
1001010110001011010101010101011010101010101110101010111010100010110010
k
Can we handle the case where the stream is
not bits, but integers, and we want the sum
of the last k elements?
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
44
Stream of positive integers
We want the sum of the last k elements
Amazon: Avg. price of last k sales
Solution:
(1) If you know all have at most m bits
Treat m bits of each integer as a separate stream
Use DGIM to count 1s in each integer ci …estimated count for i-th bit
𝑖
The sum is = 𝑚−1
𝑖=0 𝑐𝑖 2
(2) Use buckets to keep partial sums
Sum of elements in size b bucket is at most 2b
2 5 7 1 3 8 4 6 7 9 1 3 7 6 5 3 5 7 1 3 3 1 2 2 6
2 5 7 1 3 8 4 6 7 9 1 3 7 6 5 3 5 7 1 3 3 1 2 2 6 3
2 5 7 1 3 8 4 6 7 9 1 3 7 6 5 3 5 7 1 3 3 1 2 2 6 3 2
2 5 7 1 3 8 4 6 7 9 1 3 7 6 5 3 5 7 1 3 3 1 2 2 6 3 2 5
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
Idea: Sum in each
bucket is at most
2b (unless bucket
has only 1 integer)
Bucket sizes:
16 8 4 2 1
45
Sampling a fixed proportion of a stream
Sample size grows as the stream grows
Sampling a fixed-size sample
Reservoir sampling
Counting the number of 1s in the last N
elements
Exponentially increasing windows
Extensions:
Number of 1s in any last k (k < N) elements
Sums of integers in the last N elements
J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org
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